EP2428771A1 - Heuristisches Verfahren zum finden des kürzesten Weges in einer Punkt-zu-Punkt Pfadplanung - Google Patents

Heuristisches Verfahren zum finden des kürzesten Weges in einer Punkt-zu-Punkt Pfadplanung Download PDF

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Publication number
EP2428771A1
EP2428771A1 EP20110180822 EP11180822A EP2428771A1 EP 2428771 A1 EP2428771 A1 EP 2428771A1 EP 20110180822 EP20110180822 EP 20110180822 EP 11180822 A EP11180822 A EP 11180822A EP 2428771 A1 EP2428771 A1 EP 2428771A1
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Prior art keywords
obstacles
obstacle
points
bypass
node
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EP20110180822
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French (fr)
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EP2428771B1 (de
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Dominique Heurguier
Jean-Yves Poilleux
Thomas Chocardelles
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Thales SA
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Thales SA
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes

Definitions

  • the invention relates to a method for finding a shorter path with heuristics. It applies in particular to the fields of off-road itinerary planning in a complex environment.
  • complex environment designates in the description, an environment including many obstacles or areas of non-crossing. These obstacles correspond, for example, to relief elements such as mountains or crevices, hydrographic features such as lakes or rivers, or anything that makes it difficult to pass the vehicle for which a route is to be planned.
  • terrain is used to indicate that the method according to the invention is applicable for route planning road, off-road or mixed.
  • the terrain is usually represented as a graph of elementary displacements.
  • a graph corresponds for example to a mesh and is composed of nodes also called vertices.
  • a route can be determined by applying a path search method by scanning said graph.
  • a graph is usually implemented in two dimensions and takes into account the trafficability and the type of terrain encountered.
  • a grid of the graph may be associated with data indicating whether the corresponding portion of terrain is passable as well as the speed at which it can be crossed.
  • the existing graph exploration methods make it possible to solve the problem of finding a shorter path between a starting node and an arrival node.
  • the algorithm A * also called A-star, is a known example and is considered particularly effective in solving this problem. This algorithm is described in particular in the article of PE Haart, NJ Nilsson and B. Raphale entitled A Formal Basis for the Heuristic Determination of Minimum Cost Paths, IEEE Transactions of Systems Science and Cybernetics, Vol. 4, No. 2, pages 100-107, July 1968 .
  • the algorithm A * uses a cost function to select the nodes composing the route. This cost function can be broken down into two sub-functions.
  • the first sub-function is a function of evaluating the cost of the path traveled from the starting node to the current node and corresponds to the sum of the valuations of the arcs composing the shortest known path.
  • the second sub-function is a function of estimating the cost of the shortest path from the current node to the arrival node. This second function is also named heuristic function.
  • a heuristic function corresponds to a suboptimal method allowing to approach an optimal solution. It is also shown that if this heuristic function is a minor of the real cost of the shortest path, then the algorithm A * finds an optimal solution of shorter path if there is one.
  • the heuristic function used is usually the Euclidean distance between the current node and the arrival node.
  • the heuristic function based on the Euclidean distance leads to prohibitive computation times when applied to large complex terrains. Indeed, this heuristic does not take into account the macroscopic information available in the field such as obstacles or non-passable lines.
  • the circumvention of an obstacle can result in an exploration of a large number of nodes of the graph, which leads to a very large number of calculations.
  • the exploration combinatory increases very strongly. The implementation of an optimal path search then becomes very expensive in computing time.
  • some methods sometimes use a heuristic function that does not reduce the cost of the shortest path from the current node to the arrival node. This leads to a reduction in the calculation time but this does not guarantee obtaining a shorter path, the method is then suboptimal.
  • An object of the invention is in particular to overcome the aforementioned drawbacks.
  • the invention relates to a shorter path search method between a current node C and an arrival node A belonging to a graph of elementary displacements.
  • the method comprises at least the following steps: a test (201) checking whether the node C is in line-of-sight with the node A or if obstacles oppose it; determining obstacle avoidance points preventing line of sight (202) and selecting at least one of these points; the search of the heuristic path (203) between the node C and the selected bypass then between the bypass point C and the node A.
  • the obstacles are represented by their convex envelopes, said envelopes being defined by the nodes Ci corresponding to the ends of the segments composing the envelopes.
  • an obstacle is considered as preventing a line of sight between the points C and A if said obstacle intersects the segment defined by these two nodes.
  • a bypass point is selected from Cn and Cm so as to minimize the length L.
  • the invention advantageously provides that the determination of the convex envelopes obstacles is performed prior to the execution of the process or during the obstacle detection test.
  • the determination of the bypass points is made, for example, by selecting a single obstacle.
  • the determination of the bypass points is performed by successively selecting several obstacles.
  • an obstacle is selected from the obstacles intersecting if its convex envelope has the largest opening angle, the opening angle corresponding to the angle made by the two segments connecting the current point C at the two bypass points belonging to the convex hull of each obstacle.
  • an obstacle is selected from the intersecting obstacles if its convex envelope is closest to the current point C.
  • an obstacle is selected from the intersecting obstacles if the surface of its convex envelope is the largest.
  • the method comprises a step determining possible heuristic paths bypassing the convex envelopes of the obstacles of the zone to be explored, then selecting the one having the shortest distance between the current node C and the destination node A.
  • the invention also relates to a navigation device implementing the method described above.
  • the figure 1 illustrates the behavior of the algorithm A * in an environment with several obstacles.
  • the algorithm is applied to find the best path between a node of start 104 and an arrival node 105.
  • the environment is modeled using a graph 106 whose nodes and arcs are not represented.
  • Three obstacles 101, 102, 109 for example corresponding to walls, are present in the moving environment.
  • a path 103 is chosen by the algorithm A * so that said obstacles are bypassed by taking by-pass points 107, 108.
  • the set of nodes explored and having been considered as candidates for the selected path 103 is indicated on the figure using a gray area 100. It appears in particular that upstream and downstream of the first obstacle 101, a large number of candidate nodes has been analyzed.
  • the algorithms of the state of the art are not optimized to quickly find the best path in complex environments.
  • the large number of nodes tested by the algorithm requires numerous calculations and results in a very significant increase in processing time in comparison with less complex environments.
  • the method according to the invention set out below aims to analyze the environment in order to first determine the bypass points making it possible to circumvent obstacles, in order to facilitate the work of the path search algorithm and thus reduce computational complexity
  • the bypass points will not all be taken by the result path of the algorithm but guide it in the search for the best path.
  • the figure 2 presents a simplified diagram of the process according to the invention.
  • the path search method advantageously takes into account a macroscopic knowledge of non-passable zones in the heuristic function. This results in particular a sharp reduction in the number of nodes explored by a conventional path search algorithm such as the conventional algorithm A *.
  • the invention is based on the definition and use of a heuristic function that evaluates the cost of a shorter path from the current node to the arrival node by bypassing obstacles located between these two nodes if necessary. Thanks to an efficient exploration of graph modeling the displacement environment, the method according to the invention reduces significantly the exploration combinatorial by limiting the number of explored nodes of the graph for calculating a shorter path.
  • the simplified diagram of the figure 2 presents the main steps of the process according to the invention.
  • the invention uses information describing non-passable obstacles to direct the algorithm A * to search for paths that bypass these obstacles and non-passable areas to find an optimal path. This makes it possible to reduce the exploration zone and thus the number of nodes explored.
  • a first step 201 aims to detect which obstacles to take into account for determining the best path. For this, an intervisibility test is applied to determine if the current node is in line of sight with the arrival node or if at least one obstacle is between these two points.
  • a second step 202 determines one or more points of circumvention of this obstacle.
  • a third step 203 corresponds to the search for the shortest heuristic bypass path and to the evaluation of the heuristic function (length of the shortest heuristic bypass path), the bypass points detected during the second step being used in order to simplify calculations. This estimate of the shortest path is called in the following description of the heuristic path and designates an estimate of the shortest path remaining between the current node and the destination node.
  • the figure 3 gives an example of obstacle modeling by a convex envelope.
  • the latter are for example modeled using convex envelopes.
  • This type of modeling makes it possible to simplify and optimize the treatments performed by the subsequent steps. This does not disturb the behavior of the path search algorithm and reduces the number of vertexes of the polygons, since the current points or the point of arrival are not located inside the convex envelope.
  • obstacles are usually represented in graphs as polygons or polygonal lines.
  • the convex hull of an obstacle is the smallest convex polygon containing it. It is recalled that a polygon is said to be convex if all its diagonals are entirely inside the surface delimited by said polygon.
  • a diagonal of a polygon is a segment that joins two non-consecutive nodes, that is, a segment that joins two nodes and is not a side of the polygon.
  • the figure 3 gives the example of two obstacles modeled as polygons 300, 301 and their respective convex envelopes 302, 303.
  • the figure 4 illustrates a way to determine a bypass point of an obstacle.
  • a convex hull is determined for each obstacle.
  • a convex hull includes a finite number S of nodes.
  • the i-th node is noted Ci with i ⁇ [1, S].
  • a convex envelope is characterized by the nodes Ci segments constituting it.
  • Point C represents the current node and point A the arrival node.
  • the angle ⁇ i formed by segments [CA] 402 and [CCi] 401 is determined.
  • ⁇ i (CA, CCi) valued in the trigonometric direction.
  • FIGS. 5a, 5b, 5c illustrate the progress of the process steps on an example environment with two obstacles.
  • the method according to the invention uses as input parameters the geographical coordinates of the current node 500, the geographical coordinates of the arrival node 501 as well as the coordinates of the nodes of the convex envelopes 504, 505 associated with the obstacles 502, 503.
  • Obstacles are defined by polygons or polygonal lines also called polylines.
  • the convex envelopes 504, 505 of these obstacles are, for example, determined beforehand, that is to say before the execution of the process steps.
  • the determination of the convex envelopes can be performed during the execution of the method, for example during the obstacle detection step 201, in order to take account in particular of obstacles in motion.
  • the determination of the heuristic function at the current node 500 produces an evaluation of the shortest path from the current point C 500 to the arrival point A 501.
  • the intervisibility test step 201 applies, for example, a distance calculation function d (C, A) between the two points C and A. It is said that the two points C and A are in direct intervisibility if the segment [C, A] does not intersect any obstacle.
  • the step 202 for finding bypass points aims at determining two points U and V tangent to the convex hull of the obstacle closest to the point C and which is in intersection with the segment [C, A] 506.
  • the result of the intervisibility test is that an obstacle 503 intersects the segment [CA] 506.
  • the bypass point with the minimum cumulative distance is then selected.
  • the figure 5c shows that in the example, it is the point U which is retained because d1 ⁇ d2.
  • the estimation of the path between C and A is based on a heuristic usually used, that is to say the Euclidean distance d (C, A) of C to A when a minimum distance criterion is chosen or d (C, A) / Vmax when a minimum travel time criterion is chosen.
  • the estimation of the path between C and the selected bypass point U is based on a heuristic usually used, that is to say the Euclidean distance d (C, U) from C to U when a minimum distance criterion is chosen or else d (C, U) / Vmax when a minimum travel time criterion is chosen.
  • a first family of methods known as first-order methods take into account only one obstacle, even if several obstacles intersect the segment [C, A]. It is then possible to treat for example in priority the nearest obstacle or the largest obstacle, for example in the sense of the highest apparent angle.
  • a second family of methods called higher order methods take into account several obstacles intersecting the segment [C, A]. For example, it is an implementation of a method of order 1 by recursion until exhaustion of obstacles or by limiting the recursion to a given number of obstacles or to a given number of iterations.
  • an example is that after the search for the obstacles intersecting the segment [C, A], an obstacle list is constituted. One of the obstacles in this list is retained, the latter corresponding to the obstacle closest to the current node. On the basis of this obstacle, the two distances d1 and d2 are determined passing through the bypass points associated with it and the smallest distance is retained.
  • first-order selection methods may be employed by selecting in a different way one of the obstacles in the intersecting obstacle list. These methods then make it possible to optimize the heuristic path search step of the method.
  • the obstacle selected may be the one whose convex envelope has the largest opening angle, the opening angle corresponding to the angle made by the two segments connecting the current point C to the two bypass points belonging to the convex hull of each obstacle.
  • the figure 6 gives an example of a first order obstacle selection method.
  • Three obstacles 600, 601, 602 are shown but only two of them 601, 602 intersect the segment [C, A]. One of these two obstacles must be selected.
  • Another criterion can be used, for example based on the calculation of the distance of the contour of the convex envelopes of the obstacles retained and retaining the obstacle which is associated with the greatest of these values.
  • the figure 7 gives an example of a higher order obstacle selection method.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Navigation (AREA)
EP11180822.6A 2010-09-10 2011-09-09 Heuristisches Verfahren zum Finden des kürzesten Weges in einer Punkt-zu-Punkt Pfadplanung Active EP2428771B1 (de)

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FR1003631A FR2964765B1 (fr) 2010-09-10 2010-09-10 Procede de recherche de plus court chemin avec heuristique

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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130060409A1 (en) * 2010-05-17 2013-03-07 Honda Motor Co., Ltd. Control device and control method for electric vehicle
US20130253827A1 (en) * 2012-03-26 2013-09-26 Electronics And Telecommunications Research Institute Apparatus for fast path search by learning heuristic function and method thereof
EP2863177A1 (de) * 2013-10-18 2015-04-22 AEVO GmbH Berechnungsmethode eines Pfades zur Verwendung in einem Fahrzeug
EP3306431A1 (de) * 2016-10-06 2018-04-11 The Boeing Company Computerimplementiertes verfahren und system zum führen eines fahrzeugs in einem szenario mit hindernissen
EP3339806A1 (de) * 2016-12-22 2018-06-27 Gestalt Systems GmbH Navigation für fahrzeuge basierend auf der paralleln datenverarbeitung zur bestimmung kollisionsfreier wege
CN109011575A (zh) * 2018-07-04 2018-12-18 苏州玩友时代科技股份有限公司 一种自动寻路方法、装置和设备
WO2019026074A1 (en) 2017-07-31 2019-02-07 Israel Aerospace Industries Ltd. TRAJECTORY PLANNING WITHIN A TRAINED AREA
EP3623759A1 (de) * 2018-09-14 2020-03-18 The Boeing Company Computerimplementiertes verfahren und system zur bestimmung eines pfads eines fahrzeugs in einer umgebung mit hindernissen
US10740702B2 (en) 2016-01-08 2020-08-11 Oracle International Corporation Method, system, and non-transitory computer-readable medium for reducing computation time in one-to-many path searching using heuristics and limited boundary adjustment
CN114442626A (zh) * 2022-01-24 2022-05-06 深圳市拓普智造科技有限公司 一种高效的全局路径规划方法

Citations (1)

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WO2006080996A2 (en) * 2005-01-25 2006-08-03 Deere & Company Point-to-point path planning

Patent Citations (1)

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WO2006080996A2 (en) * 2005-01-25 2006-08-03 Deere & Company Point-to-point path planning

Non-Patent Citations (1)

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P.E. HAART, N.J. NILSSON, B. RAPHALE: "A FormaI Basis for the Heuristic Determination of Minimum Cost Paths", IEEE TRANSACTIONS OF SYSTEMS SCIENCE AND CYBERNETICS, vol. 4, no. 2, July 1968 (1968-07-01), pages 100 - 107

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130060409A1 (en) * 2010-05-17 2013-03-07 Honda Motor Co., Ltd. Control device and control method for electric vehicle
US9260003B2 (en) * 2010-05-17 2016-02-16 Honda Motor Co., Ltd. Control device and control method for electric vehicle
US20130253827A1 (en) * 2012-03-26 2013-09-26 Electronics And Telecommunications Research Institute Apparatus for fast path search by learning heuristic function and method thereof
US8935096B2 (en) * 2012-03-26 2015-01-13 Electronics And Telecommunications Research Institute Apparatus for fast path search by learning heuristic function and method thereof
EP2863177A1 (de) * 2013-10-18 2015-04-22 AEVO GmbH Berechnungsmethode eines Pfades zur Verwendung in einem Fahrzeug
US10740702B2 (en) 2016-01-08 2020-08-11 Oracle International Corporation Method, system, and non-transitory computer-readable medium for reducing computation time in one-to-many path searching using heuristics and limited boundary adjustment
US10352711B2 (en) 2016-10-06 2019-07-16 The Boeing Company Computer-implemented method and a system for guiding a vehicle within a scenario with obstacles
EP3306431A1 (de) * 2016-10-06 2018-04-11 The Boeing Company Computerimplementiertes verfahren und system zum führen eines fahrzeugs in einem szenario mit hindernissen
CN108225358B (zh) * 2016-12-22 2020-07-17 格式塔系统有限责任公司 交通工具导航
EP3339806A1 (de) * 2016-12-22 2018-06-27 Gestalt Systems GmbH Navigation für fahrzeuge basierend auf der paralleln datenverarbeitung zur bestimmung kollisionsfreier wege
US10466058B2 (en) 2016-12-22 2019-11-05 Gestalt Systems GmbH Navigation for vehicles
CN108225358A (zh) * 2016-12-22 2018-06-29 格式塔系统有限责任公司 交通工具导航
WO2019026074A1 (en) 2017-07-31 2019-02-07 Israel Aerospace Industries Ltd. TRAJECTORY PLANNING WITHIN A TRAINED AREA
EP3662231A4 (de) * 2017-07-31 2021-04-21 Israel Aerospace Industries Ltd. Wegplanung innerhalb eines durchlaufenen bereichs
AU2018311637B2 (en) * 2017-07-31 2024-02-01 Elta Systems Ltd. Path planning within a traversed area
CN109011575A (zh) * 2018-07-04 2018-12-18 苏州玩友时代科技股份有限公司 一种自动寻路方法、装置和设备
EP3623759A1 (de) * 2018-09-14 2020-03-18 The Boeing Company Computerimplementiertes verfahren und system zur bestimmung eines pfads eines fahrzeugs in einer umgebung mit hindernissen
US11262764B2 (en) 2018-09-14 2022-03-01 The Boeing Company Computer-implemented method and a system for defining a path for a vehicle within an environment with obstacles
CN114442626A (zh) * 2022-01-24 2022-05-06 深圳市拓普智造科技有限公司 一种高效的全局路径规划方法
CN114442626B (zh) * 2022-01-24 2024-04-26 深圳市拓普智造科技有限公司 一种全局路径规划方法

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FR2964765B1 (fr) 2016-04-15
EP2428771B1 (de) 2016-01-13
MY163420A (en) 2017-09-15
FR2964765A1 (fr) 2012-03-16

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